Publications

@inproceedings{FadiECCV2024,

title = {AdaDistill: Adaptive Knowledge Distillation for Deep Face Recognition},

author = {Fadi Boutros and Vitomir Štruc and Naser Damer},

url = {https://arxiv.org/pdf/2407.01332},

year  = {2024},

date = {2024-09-30},

booktitle = {Proceedings of the European Conference on Computer Vision (ECCV 2024)},

pages = {1-20},

abstract = {Knowledge distillation (KD) aims at improving the performance of a compact student model by distilling the knowledge from a high-performing teacher model. In this paper, we present an adaptive KD approach, namely AdaDistill, for deep face recognition. The proposed AdaDistill embeds the KD concept into the softmax loss by training the student using a margin penalty softmax loss with distilled class centers from the teacher. Being aware of the relatively low capacity of the compact student model, we propose to distill less complex knowledge at an early stage of training and more complex one at a later stage of training. This relative adjustment of the distilled knowledge is controlled by the progression of the learning capability of the student over the training iterations without the need to tune any hyper-parameters. Extensive experiments and ablation studies show that AdaDistill can enhance the discriminative learning capability of the student and demonstrate superiority over various state-of-the-art competitors on several challenging benchmarks, such as IJB-B, IJB-C, and ICCV2021-MFR},

keywords = {adaptive distillation, biometrics, CNN, deep learning, face, face recognition, knowledge distillation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{EK_Ocvirk2024,

title = {Primerjava metod za zaznavanje napadov ponovnega zajema},

author = {Krištof Ocvirk and Marko Brodarič and Peter Peer and Vitomir Struc and Borut Batagelj},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/10/ocvirkprimerjava_metod.pdf},

year  = {2024},

date = {2024-09-26},

urldate = {2024-09-26},

booktitle = {Proceedings of ERK},

pages = {1-4},

address = {Portorož, Slovenia},

abstract = {The increasing prevalence of digital identity verification has amplified the demand for robust personal document authentication systems. To obscure traces of forgery, forgers often photograph the documents after reprinting or directly capture them from a screen display. This paper is a work report for the First Competition on Presentation Attack Detection on ID Cards, held at the International Joint Conference on Biometrics 2024 (IJCB PAD-ID Card 2024). The competition aims to explore the efficacy of deep neural networks in detecting recapture attacks. The Document Liveness Challenge Dataset (DLC-2021) was utilized to train models. Several models were adapted for this task, including ViT, Xception, TRes-Net, and EVA. Among these, the Xception model achieved the best performance, showing a significantly low error rate in both attack presentation classification error and bona fide presentation classification error.},

keywords = {attacks, biometrics, CNN, deep learning, identity cards, pad},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK_PAD24,

title = {Detection of Presentation Attacks with 3D Masks Using Deep Learning},

author = {Lovro Sikošek and Marko Brodarič and Peter Peer and Vitomir Struc and Borut Batagelj},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/10/sikosekdetekcija_prezentacijskih.pdf},

year  = {2024},

date = {2024-09-25},

booktitle = {Proceedings of ERK 2024},

pages = {1-4},

address = {Portorož, Slovenia},

abstract = {This paper describes a cutting edge approach to Presentation Attack Detection (PAD) of 3D mask attacks using deep learning. We utilize a ResNeXt convolutional neural network, pre-trained on the ImageNet dataset and fine-tuned on the 3D Mask Attack Database (3DMAD). We also evaluate the model on a smaller, more general validation set containing different types of presentation attacks captured with various types of sensors. Experimental data shows that our model achieves high accuracy in distinguishing between genuine faces and mask attacks within the 3DMAD database. However, evaluation on a more general testing set reveals challenges in generalizing to new types of attacks and datasets, suggesting the need for further research to enhance model robustness.},

keywords = {biometrics, CNN, deep learning, face PAD, face recognition, pad},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SWAP_ERK_24,

title = {Prepoznava zamenjave obraza na slikah osebnih dokumentov},

author = {Leon Alessio and Marko Brodarič and Peter Peer and Vitomir Struc and Borut Batagelj},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/10/alessioprepoznava_zamenjave.pdf},

year  = {2024},

date = {2024-09-25},

booktitle = {Proceedings of ERK 2024},

pages = {1-4},

address = {Portorož, Slovenia},

abstract = {In recent years, a need for remote user authentication has emerged. Many authentication techniques are based on verifying an image of identity documents (ID). This approach mitigates the need for physical presence from both parties, making the authentication process quicker and more effective. However, it also presents challenges, such as data security and the risk of identity fraud. Attackers use many techniques to fool authentication algorithms. This paper focuses on detecting face substitution, a common and straightforward fraud technique where the perpetrator replaces the face image on the ID. Due to its simplicity, almost anyone can utilize this technique extensively. Unlike digitally altered images, these modifications are manually detectable but pose challenges for computer algorithms. To face the challenge of detecting such an attack, we extended a dataset containing original images of identity cards of 9 countries with altered images, where the original face was substituted with another face from the dataset. We developed a method to detect such tampering by identifying unusual straight lines that indicate an overlay on the ID. We then evaluated the method on our dataset. While the method showed limited success, it underscores the complexity of this problem and provides a benchmark for future research.},

keywords = {biometrics, deep learning, deep models, face PAD, face recognition, pad},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Krizaj,

title = {Discovering Interpretable Feature Directions in the Embedding Space of Face Recognition Models},

author = {Richard Plesh and Janez Križaj and Keivan Bahmani and Mahesh Banavar and Vitomir Struc and Stephanie Schuckers},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2024/08/107.pdf

https://lmi.fe.uni-lj.si/wp-content/uploads/2024/08/107-supp.pdf},

year  = {2024},

date = {2024-09-15},

booktitle = {International Joint Conference on Biometrics (IJCB 2024)},

pages = {1-10},

abstract = {Modern face recognition (FR) models, particularly their convolutional neural network based implementations, often raise concerns regarding privacy and ethics due to their “black-box” nature. To enhance the explainability of FR models and the interpretability of their embedding space, we introduce in this paper three novel techniques for discovering semantically meaningful feature directions (or axes). The first technique uses a dedicated facial-region blending procedure together with principal component analysis to discover embedding space direction that correspond to spatially isolated semantic face areas, providing a new perspective on facial feature interpretation. The other two proposed techniques exploit attribute labels to discern feature directions that correspond to intra-identity variations, such as pose, illumination angle, and expression, but do so either through a cluster analysis or a dedicated regression procedure. To validate the capabilities of the developed techniques, we utilize a powerful template decoder that inverts the image embedding back into the pixel space. Using the decoder, we visualize linear movements along the discovered directions, enabling a clearer understanding of the internal representations within face recognition models. The source code will be made publicly available.},

keywords = {biometrics, CNN, deep learning, face recognition, feature space understanding, xai},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Diffiqa_2023,

title = {DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models},

author = {Žiga Babnik and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/121.pdf

https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/121-supp.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics },

pages = {1-10},

publisher = {IEEE},

address = {Ljubljana, Slovenia},

abstract = {Modern face recognition (FR) models excel in constrained

scenarios, but often suffer from decreased performance

when deployed in unconstrained (real-world) environments

due to uncertainties surrounding the quality

of the captured facial data. Face image quality assessment

(FIQA) techniques aim to mitigate these performance

degradations by providing FR models with sample-quality

predictions that can be used to reject low-quality samples

and reduce false match errors. However, despite steady improvements,

ensuring reliable quality estimates across facial

images with diverse characteristics remains challenging.

In this paper, we present a powerful new FIQA approach,

named DifFIQA, which relies on denoising diffusion

probabilistic models (DDPM) and ensures highly competitive

results. The main idea behind the approach is to utilize

the forward and backward processes of DDPMs to perturb

facial images and quantify the impact of these perturbations

on the corresponding image embeddings for quality

prediction. Because the diffusion-based perturbations are

computationally expensive, we also distill the knowledge

encoded in DifFIQA into a regression-based quality predictor,

called DifFIQA(R), that balances performance and

execution time. We evaluate both models in comprehensive

experiments on 7 diverse datasets, with 4 target FR models

and against 10 state-of-the-art FIQA techniques with

highly encouraging results. The source code is available

from: https://github.com/LSIbabnikz/DifFIQA.},

keywords = {biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face image quality assesment, face recognition, FIQA, quality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{EFAR2023_2023,

title = {EFaR 2023: Efficient Face Recognition Competition},

author = {Jan Niklas Kolf and Fadi Boutros and Jurek Elliesen and Markus Theuerkauf and Naser Damer and Mohamad Y Alansari and Oussama Abdul Hay and Sara Yousif Alansari and Sajid Javed and Naoufel Werghi and Klemen Grm and Vitomir Struc and Fernando Alonso-Fernandez and Kevin Hernandez-Diaz and Josef Bigun and Anjith George and Christophe Ecabert and Hatef Otroshi Shahreza and Ketan Kotwal and Sébastien Marcel and Iurii Medvedev and Jin Bo and Diogo Nunes and Ahmad Hassanpour and Pankaj Khatiwada and Aafan Ahmad Toor and Bian Yang},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/CameraReady-231.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics (IJCB 2023)},

pages = {1-12},

address = {Ljubljana, Slovenia},

abstract = {This paper presents the summary of the Efficient Face

Recognition Competition (EFaR) held at the 2023 International

Joint Conference on Biometrics (IJCB 2023). The

competition received 17 submissions from 6 different teams.

To drive further development of efficient face recognition

models, the submitted solutions are ranked based on a

weighted score of the achieved verification accuracies on a

diverse set of benchmarks, as well as the deployability given

by the number of floating-point operations and model size.

The evaluation of submissions is extended to bias, crossquality,

and large-scale recognition benchmarks. Overall,

the paper gives an overview of the achieved performance

values of the submitted solutions as well as a diverse set of

baselines. The submitted solutions use small, efficient network

architectures to reduce the computational cost, some

solutions apply model quantization. An outlook on possible

techniques that are underrepresented in current solutions is

given as well.},

keywords = {biometrics, deep learning, face, face recognition, lightweight models},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SSBRC2023,

title = {Sclera Segmentation and Joint Recognition Benchmarking Competition: SSRBC 2023},

author = {Abhijit Das and Saurabh K Atreya and Aritra Mukherjee and Matej Vitek and Haiqing Li and Caiyong Wang and Zhao Guangzhe and Fadi Boutros and Patrick Siebke and Jan Niklas Kolf and Naser Damer and Ye Sun and Lu Hexin and Fab Aobo and You Sheng and Sabari Nathan and Suganya Ramamoorthy and Rampriya R S and Geetanjali G and Prinaka Sihag and Aditya Nigam and Peter Peer and Umapada Pal and Vitomir Struc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/CameraReady-233.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics (IJCB 2023)},

pages = {1-10},

address = {Ljubljana, Slovenia},

abstract = {This paper presents the summary of the Sclera Segmentation

and Joint Recognition Benchmarking Competition (SSRBC

2023) held in conjunction with IEEE International

Joint Conference on Biometrics (IJCB 2023). Different from

the previous editions of the competition, SSRBC 2023 not

only explored the performance of the latest and most advanced

sclera segmentation models, but also studied the impact

of segmentation quality on recognition performance.

Five groups took part in SSRBC 2023 and submitted a total

of six segmentation models and one recognition technique

for scoring. The submitted solutions included a wide

variety of conceptually diverse deep-learning models and

were rigorously tested on three publicly available datasets,

i.e., MASD, SBVPI and MOBIUS. Most of the segmentation

models achieved encouraging segmentation and recognition

performance. Most importantly, we observed that better

segmentation results always translate into better verification

performance.},

keywords = {biometrics, competition IJCB, computer vision, deep learning, sclera, sclera segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{UERC2023,

title = {The Unconstrained Ear Recognition Challenge 2023: Maximizing Performance and Minimizing Bias},

author = {Ziga Emersic and Tetsushi Ohki and Muku Akasaka and Takahiko Arakawa and Soshi Maeda and Masora Okano and Yuya Sato and Anjith George and Sébastien Marcel and Iyyakutti Iyappan Ganapathi and Syed Sadaf Ali and Sajid Javed and Naoufel Werghi and Selin Gök Işık and Erdi Sarıtaş and Hazim Kemal Ekenel and Valter Hudovernik and Jan Niklas Kolf and Fadi Boutros and Naser Damer and Geetanjali Sharma and Aman Kamboj and Aditya Nigam and Deepak Kumar Jain and Guillermo Cámara and Peter Peer and Vitomir Struc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/09/CameraReady-234.pdf},

year  = {2023},

date = {2023-09-01},

booktitle = {IEEE International Joint Conference on Biometrics (IJCB 2023)},

pages = {1-10},

address = {Ljubljana, Slovenia},

abstract = {The paper provides a summary of the 2023 Unconstrained

Ear Recognition Challenge (UERC), a benchmarking

effort focused on ear recognition from images acquired

in uncontrolled environments. The objective of the challenge

was to evaluate the effectiveness of current ear recognition

techniques on a challenging ear dataset while analyzing

the techniques from two distinct aspects, i.e., verification

performance and bias with respect to specific demographic

factors, i.e., gender and ethnicity. Seven research

groups participated in the challenge and submitted

a seven distinct recognition approaches that ranged from

descriptor-based methods and deep-learning models to ensemble

techniques that relied on multiple data representations

to maximize performance and minimize bias. A comprehensive

investigation into the performance of the submitted

models is presented, as well as an in-depth analysis of

bias and associated performance differentials due to differences

in gender and ethnicity. The results of the challenge

suggest that a wide variety of models (e.g., transformers,

convolutional neural networks, ensemble models) is capable

of achieving competitive recognition results, but also

that all of the models still exhibit considerable performance

differentials with respect to both gender and ethnicity. To

promote further development of unbiased and effective ear

recognition models, the starter kit of UERC 2023 together

with the baseline model, and training and test data is made

available from: http://ears.fri.uni-lj.si/.},

keywords = {biometrics, competition, computer vision, deep learning, ear, ear biometrics, UERC 2023},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{IWBF2023_Marija,

title = {Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models},

author = {Marija Ivanovska and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2023/03/IWBF2023_Morphing.pdf},

year  = {2023},

date = {2023-02-28},

booktitle = {Proceedings of the International Workshop on Biometrics and Forensics (IWBF)},

pages = {1-6},

abstract = {Morphed face images have recently become a growing concern for existing face verification systems, as they are relatively easy to generate and can be used to impersonate someone's identity for various malicious purposes. Efficient Morphing Attack Detection (MAD) that generalizes well across different morphing techniques is, therefore, of paramount importance. Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks. To address this problem, we propose a novel, diffusion--based MAD method in this paper that learns only from the characteristics of bona fide images. Various forms of morphing attacks are then detected by our model as out-of-distribution samples. We perform rigorous experiments over four different datasets (CASIA-WebFace, FRLL-Morphs, FERET-Morphs and FRGC-Morphs) and compare the proposed solution to both discriminatively-trained and once-class MAD models. The experimental results show that our MAD model achieves highly competitive results on all considered datasets.},

keywords = {biometrics, deep learning, denoising diffusion probabilistic models, diffusion, face, face morphing attack, morphing attack, morphing attack detection},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{TomasevicIJCBBiOcular,

title = {BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images},

author = {Darian Tomašević and Peter Peer and Vitomir Štruc },

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/12/BiModal_StyleGAN.pdf

https://arxiv.org/pdf/2205.01536.pdf},

year  = {2022},

date = {2022-10-20},

urldate = {2022-10-20},

booktitle = {IEEE/IAPR International Joint Conference on Biometrics (IJCB 2022) },

pages = {1-10},

abstract = {Current state-of-the-art segmentation techniques for ocular images are critically dependent on large-scale annotated datasets, which are labor-intensive to gather and often raise privacy concerns. In this paper, we present a novel framework, called BiOcularGAN, capable of generating synthetic large-scale datasets of photorealistic (visible light and near-infrared) ocular images, together with corresponding segmentation labels to address these issues. At its core, the framework relies on a novel Dual-Branch StyleGAN2 (DB-StyleGAN2) model that facilitates bimodal image generation, and a Semantic Mask Generator (SMG) component that produces semantic annotations by exploiting latent features of the DB-StyleGAN2 model. We evaluate BiOcularGAN through extensive experiments across five diverse ocular datasets and analyze the effects of bimodal data generation on image quality and the produced annotations. Our experimental results show that BiOcularGAN is able to produce high-quality matching bimodal images and annotations (with minimal manual intervention) that can be used to train highly competitive (deep) segmentation models (in a privacy aware-manner) that perform well across multiple real-world datasets. The source code for the BiOcularGAN framework is publicly available at: https://github.com/dariant/BiOcularGAN.},

keywords = {biometrics, CNN, data synthesis, deep learning, ocular, segmentation, StyleGAN, synthetic data},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ICPR2022,

title = {FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration},

author = {Žiga Babnik and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/06/ICPR_2022___paper-17.pdf},

year  = {2022},

date = {2022-05-17},

urldate = {2022-05-17},

booktitle = {IAPR International Conference on Pattern Recognition (ICPR)},

abstract = {Recent state-of-the-art face recognition (FR) approaches have achieved impressive performance, yet unconstrained face recognition still represents an open problem. Face image quality assessment (FIQA) approaches aim to estimate the quality of the input samples that can help provide information on the confidence of the recognition decision and eventually lead to improved results in challenging scenarios. While much progress has been made in face image quality assessment in recent years, computing reliable quality scores for diverse facial images and FR models remains challenging. In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent. As such, the proposed approach is the first to link image quality to adversarial attacks. Comprehensive (cross-model as well as model-specific) experiments are conducted with four benchmark datasets, i.e., LFW, CFP–FP, XQLFW and IJB–C, four FR models, i.e., CosFace, ArcFace, CurricularFace and ElasticFace and in comparison to seven state-of-the-art FIQA methods to demonstrate the performance of FaceQAN. Experimental results show that FaceQAN achieves competitive results, while exhibiting several desirable characteristics. The source code for FaceQAN will be made publicly available.},

keywords = {adversarial examples, adversarial noise, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{EUSIPCO_2022,

title = {Assessing Bias in Face Image Quality Assessment},

author = {Žiga Babnik and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/06/EUSIPCO_2022___paper.pdf},

year  = {2022},

date = {2022-05-16},

urldate = {2022-05-16},

booktitle = {EUSIPCO 2022},

abstract = {Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance  by providing additional information about sample quality. 

Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias. It is therefore likely that such problems are also present with FIQA techniques. To investigate the demographic biases associated with FIQA approaches, this paper presents a comprehensive study involving a variety of quality assessment methods (general-purpose image quality assessment, supervised face quality assessment, and unsupervised face quality assessment methods) and three diverse state-of-the-art FR models. 

Our analysis on the Balanced Faces in the Wild (BFW) dataset shows that all techniques considered are affected more by variations in race than sex. While the general-purpose image quality assessment methods appear to be less biased with respect to the two demographic factors considered, the supervised and unsupervised face image quality assessment methods both show strong bias with a tendency to favor white individuals (of either sex). In addition, we found that methods that are less racially biased perform worse overall. This suggests that the observed bias in FIQA methods is to a significant extent related to the underlying face recognition system.},

keywords = {bias, bias analysis, biometrics, face image quality assessment, face recognition, FIQA, image quality assessment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{KinEars,

title = {Kinship Verification from Ear Images: An Explorative Study with Deep Learning Models},

author = {Grega Dvoršak and Ankita Dwivedi and Vitomir Štruc and Peter Peer and Žiga Emeršič},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2022/03/Gregovi_Uhlji_Template-2.pdf},

year  = {2022},

date = {2022-04-21},

urldate = {2022-04-21},

booktitle = {International Workshop on Biometrics and Forensics (IWBF)},

pages = {1--6},

abstract = {The analysis of kin relations from visual data represents a challenging research problem with important real-world applications. However, research in this area has mostly been limited to the analysis of facial images, despite the potential of other physical (human) characteristics for this task. In this paper, we therefore study the problem of kinship verification from ear images and investigate whether salient appearance characteristics, useful for this task, can be extracted from ear data. To facilitate the study, we introduce a novel dataset, called KinEar, that contains data from 19 families with each family member having from 15 to 31 ear images. Using the KinEar data, we conduct experiments using a Siamese training setup and  5 recent deep learning backbones. The results of our experiments suggests that ear images  represent a viable alternative to other modalities for kinship verification, as 4 out of 5 considered  models reach a performance of over 60% in terms of the Area Under the Receiver Operating Characteristics (ROC-AUC). },

keywords = {biometrics, CNN, deep learning, ear, ear biometrics, kinear, kinship, kinship recognition, transformer},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK_Marija_2021,

title = {A Comparative Study on Discriminative and One--Class Learning Models for Deepfake Detection},

author = {Marija Ivanovska and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2021/10/ERK_2021__A_Comparative_Study_of_Discriminative_and_One__Class_Learning_Models_for_Deepfake_Detection.pdf},

year  = {2021},

date = {2021-09-20},

booktitle = {Proceedings of ERK 2021},

pages = {1--4},

abstract = {Deepfakes or manipulated face images, where a donor's face is swapped with the face of a target person, have gained enormous popularity among the general public recently. With the advancements in  artificial intelligence and generative modeling  

such images can nowadays be easily generated and used to spread misinformation and harm individuals, businesses or society. As the tools for generating deepfakes are rapidly improving, it is critical for deepfake detection models to be able to recognize advanced, sophisticated data manipulations, including those that have not been seen during  training. In this paper, we explore the use of one--class learning models as an alternative to discriminative methods for the detection of deepfakes. We conduct a  comparative study with three popular deepfake datasets and investigate the performance of selected (discriminative and one-class) detection models in matched- and cross-dataset experiments. Our results show that disciminative models significantly outperform one-class models when training and testing data come from the same dataset, but degrade considerably when the characteristics of the testing data deviate from the training setting. In such cases, one-class models tend to generalize much better.},

keywords = {biometrics, comparative study, computer vision, deepfake detection, deepfakes, detection, face, one-class learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{MFR_IJCB2021,

title = {MFR 2021: Masked Face Recognition Competition},

author = {Fadi Boutros and Naser Damer and Jan Niklas Kolf and Kiran Raja and Florian Kirchbuchner and Raghavendra Ramachandra and Arjan Kuijper and Pengcheng Fang and Chao Zhang and Fei Wang and David Montero and Naiara Aginako and Basilio Sierra and Marcos Nieto and Mustafa Ekrem Erakin and Ugur Demir and Hazım Kemal Ekenel and Asaki Kataoka and Kohei Ichikawa and Shizuma Kubo and Jie Zhang and Mingjie He and Dan Han and Shiguang Shan and Klemen Grm and Vitomir Štruc and Sachith Seneviratne and Nuran Kasthuriarachchi and Sanka Rasnayaka and Pedro C. Neto and Ana F. Sequeira and Joao Ribeiro Pinto and Mohsen Saffari and Jaime S. Cardoso},

url = {https://ieeexplore.ieee.org/iel7/9484326/9484328/09484337.pdf?casa_token=OOL4s274P0YAAAAA:XE7ga2rP_wNom2Zeva75ZwNwN-HKz6kF1HZtkpzrdTdz36eaGcLffWkzOgIe3xU2PqaU30qTLws},

doi = {10.1109/IJCB52358.2021.9484337},

year  = {2021},

date = {2021-08-01},

booktitle = {Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021)},

abstract = {This paper presents a summary of the Masked Face Recognition Competitions (MFR) held within the 2021 International Joint Conference on Biometrics (IJCB 2021). The competition attracted a total of 10 participating teams with valid submissions. The affiliations of these teams are diverse and associated with academia and industry in nine different countries. These teams successfully submitted 18 valid solutions. The competition is designed to motivate solutions aiming at enhancing the face recognition accuracy of masked faces. Moreover, the competition considered the deployability of the proposed solutions by taking the compactness of the face recognition models into account. A private dataset representing a collaborative, multisession, real masked, capture scenario is used to evaluate the submitted solutions. In comparison to one of the topperforming academic face recognition solutions, 10 out of the 18 submitted solutions did score higher masked face verification accuracy.

},

keywords = {biometrics, face recognition, masks},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{NIR_IJCB2021,

title = {NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization},

author = {Caiyong Wang and Yunlong Wang and Kunbo Zhang and Jawad Muhammad and Tianhao Lu and Qi Zhang and Qichuan Tian and Zhaofeng He and Zhenan Sun and Yiwen Zhang and Tianbao Liu and Wei Yang and Dongliang Wu and Yingfeng Liu and Ruiye Zhou and Huihai Wu and Hao Zhang and Junbao Wang and Jiayi Wang and Wantong Xiong and Xueyu Shi and Shao Zeng and Peihua Li and Haodong Sun and Jing Wang and Jiale Zhang and Qi Wang and Huijie Wu and Xinhui Zhang and Haiqing Li and Yu Chen and Liang Chen and Menghan Zhang and Ye Sun and Zhiyong Zhou and Fadi Boutros and Naser Damer and Arjan Kuijper and Juan Tapia and Andres Valenzuela and Christoph Busch and Gourav Gupta and Kiran Raja and Xi Wu and Xiaojie Li and Jingfu Yang and Hongyan Jing and Xin Wang and Bin Kong and Youbing Yin and Qi Song and Siwei Lyu and Shu Hu and Leon Premk and Matej Vitek and Vitomir Štruc and Peter Peer and Jalil Nourmohammadi Khiarak and Farhang Jaryani and Samaneh Salehi Nasab and Seyed Naeim Moafinejad and Yasin Amini and Morteza Noshad},

url = {https://ieeexplore.ieee.org/iel7/9484326/9484328/09484336.pdf?casa_token=FOKx4ltO-hYAAAAA:dCkNHfumDzPGkAipRdbppNWpzAiUYUrJL6OrAjNmimTxUA0Vmx311-3-J3ej7YQc_zONxEO-XKo},

doi = {10.1109/IJCB52358.2021.9484336},

year  = {2021},

date = {2021-08-01},

booktitle = {Proceedings of the IEEE International Joint Conference on Biometrics (IJCB 2021)},

abstract = {For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research.},

keywords = {biometrics, competition, iris, segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{BortolatoFG2020,

title = {Learning privacy-enhancing face representations through feature disentanglement},

author = {Blaž Bortolato and Marija Ivanovska and Peter Rot and Janez Križaj and Philipp Terhorst and Naser Damer and Peter Peer and Vitomir Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/07/FG2020___Learning_privacy_enhancing_face_representations_through_feature_disentanglement-1.pdf

},

year  = {2020},

date = {2020-11-04},

booktitle = {Proceedings of FG 2020},

publisher = {IEEE},

abstract = {Convolutional Neural Networks (CNNs) are today the de-facto standard for extracting compact and discriminative face representations (templates) from images in automatic face recognition systems. Due to the characteristics of CNN models, the generated representations typically encode a multitude of information ranging from identity to soft-biometric attributes, such as age, gender or ethnicity. However, since these representations were computed for the purpose of identity recognition only, the soft-biometric information contained in the templates represents a serious privacy risk. To mitigate this problem, we present in this paper a privacy-enhancing approach capable of suppressing potentially sensitive soft-biometric information in face representations without significantly compromising identity information. Specifically, we introduce a Privacy-Enhancing Face-Representation learning Network (PFRNet) that disentangles identity from attribute information in face representations and consequently allows to efficiently suppress soft-biometrics in face templates. We demonstrate the feasibility of PFRNet on the problem of gender suppression and show through rigorous experiments on the CelebA, Labeled Faces in the Wild (LFW) and Adience datasets that the proposed disentanglement-based approach is highly effective and improves significantly on the existing state-of-the-art.},

keywords = {autoencoder, biometrics, CNN, disentaglement, face recognition, PFRNet, privacy, representation learning},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{SSBC2020,

title = {SSBC 2020: Sclera Segmentation Benchmarking Competition in the Mobile Environment},

author = {M. Vitek and A. Das and Y. Pourcenoux and A. Missler and C. Paumier and S. Das and I. De Ghosh and D. R. Lucio and L. A. Zanlorensi Jr. and D. Menotti and F. Boutros and N. Damer and J. H. Grebe and A. Kuijper and J. Hu and Y. He and C. Wang and H. Liu and Y. Wang and Z. Sun and D. Osorio-Roig and C. Rathgeb and C. Busch and J. Tapia and A.~Valenzuela and G. Zampoukis and L. Tsochatzidis and I. Pratikakis and S. Nathan and R. Suganya and V. Mehta and A. Dhall and K. Raja and G. Gupta and J. N. Khiarak and M. Akbari-Shahper and F. Jaryani and M. Asgari-Chenaghlu and R. Vyas and S. Dakshit and S. Dakshit and P. Peer and U. Pal and V. Štruc},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/11/IJCB_SSBC_2020.pdf},

year  = {2020},

date = {2020-09-28},

booktitle = {International Joint Conference on Biometrics (IJCB 2020)},

pages = {1--10},

abstract = {The paper presents a summary of the 2020 Sclera Segmentation Benchmarking Competition (SSBC), the 7th in the series of group benchmarking efforts centred around the problem of sclera segmentation. Different from previous editions, the goal of SSBC 2020 was to evaluate the performance of sclera-segmentation models on images captured with mobile devices. The competition was used as a platform to assess the sensitivity of existing models to i) differences in mobile devices used for image capture and ii) changes in the ambient acquisition conditions. 26 research groups registered for SSBC 2020, out of which 13 took part in the final round and submitted a total of 16 segmentation models for scoring. These included a wide variety of deep-learning solutions as well as one approach based on standard image processing techniques. Experiments were conducted with three recent datasets. Most of the segmentation models achieved relatively consistent  performance across images captured with different mobile devices (with slight differences across devices), but struggled most with  low-quality images captured in challenging ambient conditions, i.e., in an indoor environment and with poor lighting. },

keywords = {biometrics, competition IJCB, ocular, sclera, segmentation, SSBC},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{GrmEUSIPCO2020,

title = {Analysis of Race and Gender Bias in Deep Age Estimation Model},

author = {Andraž Puc and Vitomir Štruc and Klemen Grm},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2020/07/race_and_gender_bias_eusipco-2.pdf},

year  = {2020},

date = {2020-09-01},

booktitle = {Proceedings of EUSIPCO 2020},

abstract = {Due to advances in deep learning and convolutional neural networks (CNNs) there has been significant progress in the field of visual age estimation from face images over recent years. While today's models are able to achieve considerable age estimation accuracy, their behaviour, especially with respect to specific demographic groups is still not well understood. In this paper, we take a deeper look at CNN-based age estimation models and analyze their performance across different race and gender groups. We use two publicly available off-the-shelf age estimation models, i.e., FaceNet and WideResNet, for our study and analyze their performance on the UTKFace and APPA-REAL datasets. We partition face images into sub-groups based on race, gender and combinations of race and gender. We then compare age estimation results and find that there are noticeable differences in performance across demographics. Specifically, our results show that age estimation accuracy is consistently higher for men than for women, while race does not appear to have consistent effects on the tested models across different test datasets.

},

keywords = {age estimation, bias, bias analysis, biometrics, face analysis},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{emervsivc2019unconstrained,

title = {The Unconstrained Ear Recognition Challenge 2019},

author = {Žiga Emeršič and A. Kumar S. V. and B. S. Harish and W. Gutfeter and J. N. Khiarak and A. Pacut and E. Hansley and M. Pamplona Segundo and S. Sarkar and H. Park and G. Pyo Nam and I. J. Kim and S.G. Sangodkar and U. Kacar and M. Kirci and L. Yuan and J. Yuan and H. Zhao and F. Lu and J. Mao and X. Zhang and D. Yaman and F. I. Eyiokur and K. B. Ozler and H. K. Ekenel and D. Paul Chowdhury and S. Bakshi and P. K. Sa and B. Majhni and P. Peer and V. Štruc},

url = {https://arxiv.org/pdf/1903.04143.pdf},

year  = {2019},

date = {2019-06-01},

booktitle = {International Conference on Biometrics (ICB 2019)},

journal = {arXiv preprint arXiv:1903.04143},

abstract = {This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i.e. gender and ethnicity. Research groups from 12 institutions entered the competition and submitted a total of 13 recognition approaches ranging from descriptor-based methods to deep-learning models. The majority of submissions focused on ensemble based methods combining either representations from multiple deep models or hand-crafted with learned image descriptors. Our analysis shows that methods incorporating deep learning models clearly outperform techniques relying solely on hand-crafted descriptors, even though both groups of techniques exhibit similar behaviour when it comes to robustness to various covariates, such presence of occlusions, changes in (head) pose, or variability in image resolution. The results of the challenge also show that there has been considerable progress since the first UERC in 2017, but that there is still ample room for further research in this area.},

keywords = {biometrics, ear, ear recognitoin, uerc 2019},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lozej2019influence,

title = {Influence of segmentation on deep iris recognition performance},

author = {Juš Lozej and Dejan Štepec and Vitomir Štruc and Peter Peer},

url = {https://arxiv.org/pdf/1901.10431.pdf},

year  = {2019},

date = {2019-03-01},

booktitle = {7th IAPR/IEEE International Workshop on Biometrics and Forensics (IWBF 2019)},

journal = {arXiv preprint arXiv:1901.10431},

abstract = {Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies on methodologies akin to traditional iris recognition pipelines. Hence, the proposed models do not approach iris recognition in an end-to-end manner, but rather use standard heuristic iris segmentation (and unwrapping) techniques to produce normalized inputs for the deep learning models. However, because deep learning is able to model very complex data distributions and nonlinear data changes, an obvious question arises. How important is the use of traditional segmentation methods in a deep learning setting? To answer this question, we present in this paper an empirical analysis of the impact of iris segmentation on the performance of deep learning models using a simple two stage pipeline consisting of a segmentation and a recognition step. We evaluate how the accuracy of segmentation influences recognition performance but also examine if segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for the experiments and report several interesting findings.},

keywords = {biometrics, iris, ocular, segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{rot2018deep,

title = {Deep multi-class eye segmentation for ocular biometrics},

author = {Peter Rot and Žiga Emeršič and Vitomir Struc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/MultiClassReduced.pdf},

year  = {2018},

date = {2018-07-01},

booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},

pages = {1--8},

organization = {IEEE},

abstract = {Segmentation techniques for ocular biometrics typically focus on finding a single eye region in the input image at the time. Only limited work has been done on multi-class eye segmentation despite a number of obvious advantages. In this paper we address this gap and present a deep multi-class eye segmentation model build around the SegNet architecture. We train the model on a small dataset (of 120 samples) of eye images and observe it to generalize well to unseen images and to ensure highly accurate segmentation results. We evaluate the model on the Multi-Angle Sclera Database (MASD) dataset and describe comprehensive experiments focusing on: i) segmentation performance, ii) error analysis, iii) the sensitivity of the model to changes in view direction, and iv) comparisons with competing single-class techniques. Our results show that the proposed model is viable solution for multi-class eye segmentation suitable for recognition (multi-biometric) pipelines based on ocular characteristics.},

keywords = {biometrics, eye, ocular, sclera, segmentation},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lozej2018end,

title = {End-to-end iris segmentation using U-Net},

author = {Juš Lozej and Blaž Meden and Vitomir Struc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/IWOBI_2018_paper_15.pdf},

year  = {2018},

date = {2018-07-01},

booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},

pages = {1--6},

organization = {IEEE},

abstract = {Iris segmentation is an important research topic that received significant attention from the research community over the years. Traditional iris segmentation techniques have typically been focused on hand-crafted procedures that, nonetheless, achieved remarkable segmentation performance even with images captured in difficult settings. With the success of deep-learning models, researchers are increasingly looking towards convolutional neural networks (CNNs) to further improve on the accuracy of existing iris segmentation techniques and several CNN-based techniques have already been presented recently in the literature. In this paper we also consider deep-learning models for iris segmentation and present an iris segmentation approach based on the popular U-Net architecture. Our model is trainable end-to-end and, hence, avoids the need for hand designing the segmentation procedure. We evaluate the model on the CASIA dataset and report encouraging results in comparison to existing techniques used in this area.},

keywords = {biometrics, CNN, convolutional neural networks, iris, ocular, U-net},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{emervsivc2018towards,

title = {Towards Accessories-Aware Ear Recognition},

author = {Žiga Emeršič and Nil Oleart Playa and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/iwobi-2018-inpaint-1.pdf},

doi = {10.1109/IWOBI.2018.8464138},

year  = {2018},

date = {2018-03-01},

booktitle = {2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI)},

pages = {1--8},

organization = {IEEE},

abstract = {Automatic ear recognition is gaining popularity within the research community due to numerous desirable properties, such as high recognition performance, the possibility of capturing ear images at a distance and in a covert manner, etc. Despite this popularity and the corresponding research effort that is being directed towards ear recognition technology, open problems still remain. One of the most important issues stopping ear recognition systems from being widely available are ear occlusions and accessories. Ear accessories not only mask biometric features and by this reduce the overall recognition performance, but also introduce new non-biometric features that can be exploited for spoofing purposes. Ignoring ear accessories during recognition can, therefore, present a security threat to ear recognition and also adversely affect performance. Despite the importance of this topic there has been, to the best of our knowledge, no ear recognition studies that would address these problems. In this work we try to close this gap and study the impact of ear accessories on the recognition performance of several state-of-the-art ear recognition techniques. We consider ear accessories as a tool for spoofing attacks and show that CNN-based recognition approaches are more susceptible to spoofing attacks than traditional descriptor-based approaches. Furthermore, we demonstrate that using inpainting techniques or average coloring can mitigate the problems caused by ear accessories and slightly outperforms (standard) black color to mask ear accessories.},

keywords = {accessories, biometrics, ear recognition},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{ERK2017,

title = {Do it Yourself: Building a Low-Cost Iris Recognition System at Home Using Off-The-Shelf Components},

author = {Primož Lavrič and Žiga Emeršič and Blaž Meden and Vitomir Štruc and Peter Peer},

url = {https://lmi.fe.uni-lj.si/wp-content/uploads/2019/08/lavricdo_it.pdf},

year  = {2017},

date = {2017-09-01},

booktitle = {Electrotechnical and Computer Science Conference ERK 2017},

abstract = {Among the different biometric traits that can be used for person recognition,  the human iris is generally consid-ered to be among the most accurate.   However,  despite a plethora of desirable characteristics, iris recognition is not widely as widely used as competing biometric modalities likely due to the high cost of existing commercial iris-recognition systems. In this paper we contribute towards the availability of low-cost iris recognition systems and present a prototype system built using off-the-shelf components.  We describe the prototype device, the pipeline used for iris recognition, evaluate the performance of our solution on a small in-house dataset and discuss directions for future work.  The current version of our prototype includes complete hardware and software implementations and has a combined bill-of-materials of 110 EUR. 



},

keywords = {biometrics, iris, sensor design},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{emervsivc2017unconstrained,

title = {The unconstrained ear recognition challenge},

author = {Žiga Emeršič and Dejan Štepec and Vitomir Štruc and Peter Peer and Anjith George and Adii Ahmad and Elshibani Omar and Terrance E. Boult and Reza Safdaii and Yuxiang Zhou and others Stefanos Zafeiriou and Dogucan Yaman and Fevziye I. Eyoikur and Hazim K. Ekenel},

url = {https://arxiv.org/pdf/1708.06997.pdf},

year  = {2017},

date = {2017-09-01},

booktitle = {2017 IEEE International Joint Conference on Biometrics (IJCB)},

pages = {715--724},

organization = {IEEE},

abstract = {In this paper we present the results o f the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem o f person recognition from ear images captured in uncontrolled conditions. The goal o f the challenge was to assess the performance of existing ear recognition techniques on a challenging largescale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques fo r the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity o f the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer o f the UERC was found to ensure robust performance on a smaller part o f the dataset (with 180 subjects) regardless o f image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.

},

keywords = {biometrics, competition, ear recognition, IJCB, uerc, unconstrained ear recognition challenge},

pubstate = {published},

tppubtype = {inproceedings}

}